A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India

Shweta Vincent; Babitha Ganesh; Sameena Pathan; Vishwajeet Kulkarni; Parth Sirohi; Tushar Agarwal; Silvia Raquel Garcia Benitez

doi:10.1109/ICARES60489.2023.10329911

A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India

Shweta Vincent^*
, Babitha Ganesh
, Sameena Pathan
, Vishwajeet Kulkarni
, Parth Sirohi
, Tushar Agarwal
, Silvia Raquel Garcia Benitez

^*Corresponding author for this work

Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This article presents a light-weight ANN model for the creation of a landslide susceptibility map (LSM) for the district of Idukki in the South Indian state of Kerala. The landslide conditioning factors (LCF) considered for the creation, training, validation and testing of the LSM are elevation, slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), rainfall, topographic ruggedness index (TRI), geology, soil type and land use and land cover. The Frequency Ratio (FR) analysis has been carried out on the LCFs and those having the highest Predictive Rate (PR) have been determined as aspect, slope, rainfall and soil type. Once the LSM is created, it is tested using landslide and non-landslide points using the proposed ANN model which yields an accuracy of 83.5%. Future scope in this work is to improve the accuracy of the model by using metaheuristic algorithms for optimization of weights of the ANN model.

Original language	English
Title of host publication	2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350344004
DOIs	https://doi.org/10.1109/ICARES60489.2023.10329911
Publication status	Published - 2023
Event	2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023 - Hybrid, Bali, Indonesia Duration: 26-10-2023 → 27-10-2023

Publication series

Name	2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023

Conference

Conference	2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023
Country/Territory	Indonesia
City	Hybrid, Bali
Period	26-10-23 → 27-10-23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing
Aerospace Engineering
Electrical and Electronic Engineering
Instrumentation

Access to Document

10.1109/ICARES60489.2023.10329911

Cite this

Vincent, S., Ganesh, B., Pathan, S., Kulkarni, V., Sirohi, P., Agarwal, T., & Benitez, S. R. G. (2023). A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India. In 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023 (2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARES60489.2023.10329911

Vincent, Shweta ; Ganesh, Babitha ; Pathan, Sameena et al. / A Light-Weight ANN Model for Landslide Detection : A Case Study of Idukki, India. 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023).

@inproceedings{d862e08f0b4d43829c967277ae9d2ffe,

title = "A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India",

abstract = "This article presents a light-weight ANN model for the creation of a landslide susceptibility map (LSM) for the district of Idukki in the South Indian state of Kerala. The landslide conditioning factors (LCF) considered for the creation, training, validation and testing of the LSM are elevation, slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), rainfall, topographic ruggedness index (TRI), geology, soil type and land use and land cover. The Frequency Ratio (FR) analysis has been carried out on the LCFs and those having the highest Predictive Rate (PR) have been determined as aspect, slope, rainfall and soil type. Once the LSM is created, it is tested using landslide and non-landslide points using the proposed ANN model which yields an accuracy of 83.5\%. Future scope in this work is to improve the accuracy of the model by using metaheuristic algorithms for optimization of weights of the ANN model.",

author = "Shweta Vincent and Babitha Ganesh and Sameena Pathan and Vishwajeet Kulkarni and Parth Sirohi and Tushar Agarwal and Benitez, \{Silvia Raquel Garcia\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023 ; Conference date: 26-10-2023 Through 27-10-2023",

year = "2023",

doi = "10.1109/ICARES60489.2023.10329911",

language = "English",

series = "2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023",

address = "United States",

}

Vincent, S, Ganesh, B, Pathan, S, Kulkarni, V, Sirohi, P, Agarwal, T & Benitez, SRG 2023, A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India. in 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023. 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023, Hybrid, Bali, Indonesia, 26-10-23. https://doi.org/10.1109/ICARES60489.2023.10329911

A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India. / Vincent, Shweta; Ganesh, Babitha; Pathan, Sameena et al.
2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Light-Weight ANN Model for Landslide Detection

T2 - 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023

AU - Vincent, Shweta

AU - Ganesh, Babitha

AU - Pathan, Sameena

AU - Kulkarni, Vishwajeet

AU - Sirohi, Parth

AU - Agarwal, Tushar

AU - Benitez, Silvia Raquel Garcia

PY - 2023

Y1 - 2023

N2 - This article presents a light-weight ANN model for the creation of a landslide susceptibility map (LSM) for the district of Idukki in the South Indian state of Kerala. The landslide conditioning factors (LCF) considered for the creation, training, validation and testing of the LSM are elevation, slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), rainfall, topographic ruggedness index (TRI), geology, soil type and land use and land cover. The Frequency Ratio (FR) analysis has been carried out on the LCFs and those having the highest Predictive Rate (PR) have been determined as aspect, slope, rainfall and soil type. Once the LSM is created, it is tested using landslide and non-landslide points using the proposed ANN model which yields an accuracy of 83.5%. Future scope in this work is to improve the accuracy of the model by using metaheuristic algorithms for optimization of weights of the ANN model.

AB - This article presents a light-weight ANN model for the creation of a landslide susceptibility map (LSM) for the district of Idukki in the South Indian state of Kerala. The landslide conditioning factors (LCF) considered for the creation, training, validation and testing of the LSM are elevation, slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), rainfall, topographic ruggedness index (TRI), geology, soil type and land use and land cover. The Frequency Ratio (FR) analysis has been carried out on the LCFs and those having the highest Predictive Rate (PR) have been determined as aspect, slope, rainfall and soil type. Once the LSM is created, it is tested using landslide and non-landslide points using the proposed ANN model which yields an accuracy of 83.5%. Future scope in this work is to improve the accuracy of the model by using metaheuristic algorithms for optimization of weights of the ANN model.

UR - https://www.scopus.com/pages/publications/85180541580

UR - https://www.scopus.com/pages/publications/85180541580#tab=citedBy

U2 - 10.1109/ICARES60489.2023.10329911

DO - 10.1109/ICARES60489.2023.10329911

M3 - Conference contribution

AN - SCOPUS:85180541580

T3 - 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023

BT - 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 26 October 2023 through 27 October 2023

ER -

Vincent S, Ganesh B, Pathan S, Kulkarni V, Sirohi P, Agarwal T et al. A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India. In 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2023). doi: 10.1109/ICARES60489.2023.10329911

A Light-Weight ANN Model for Landslide Detection: A Case Study of Idukki, India

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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